scholarly journals Sonar image quality evaluation using deep neural network

2021 ◽  
Author(s):  
Huiqing Zhang ◽  
Shuo Li ◽  
Donghao Li ◽  
Zichen Wang ◽  
Qixiang Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Deep Neural Network ◽  
Quality Evaluation ◽  
Image Quality Evaluation ◽  
Sonar Image

scholarly journals Three-Dimensional Image Quality Evaluation and Optimization Based on Convolutional Neural Network

2021 ◽  
Vol 38 (4) ◽  
pp. 1041-1049
Author(s):  
Xiujuan Luo
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Convolutional Neural Network ◽  
3D Imaging ◽  
Quality Evaluation ◽  
Three Dimensional ◽  
3D Image ◽  
3D Images ◽  
Image Quality Evaluation ◽  
Global And Local

Currently, three-dimensional (3D) imaging has been successfully applied in medical health, movie viewing, games, and military. To make 3D images more pleasant to the eyes, the accurate judgement of image quality becomes the key step in content preparation, compression, and transmission in 3D imaging. However, there is not yet a satisfactory evaluation method that objectively assesses the quality of 3D images. To solve the problem, this paper explores the evaluation and optimization of 3D image quality based on convolutional neural network (CNN). Specifically, a 3D image quality evaluation model was constructed, and a 3D image quality evaluation algorithm was proposed based on global and local features. Next, the authors expounded on the preprocessing steps of salient regions in images, depicted the fusion process between global and local quality evaluations, and provided the way to process 3D image samples and acquire contrast-distorted images. The proposed algorithm was proved effective through experiments.

scholarly journals Nonreference Image Quality Evaluation Algorithm Based on Wavelet Convolutional Neural Network and Information Entropy

Entropy ◽  
2019 ◽  
Vol 21 (11) ◽  
pp. 1070 ◽  
Author(s):  
Jinhua Liu ◽  
Mulian Xu ◽  
Xinye Xu ◽  
Yuanyuan Huang
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Image Quality ◽  
Information Entropy ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Visual Quality ◽  
Image Quality Evaluation ◽  
Band Image

The image quality evaluation method, based on the convolutional neural network (CNN), achieved good evaluation performance. However, this method can easily lead the visual quality of image sub-blocks to change with the spatial position after the image is processed by various distortions. Consequently, the visual quality of the entire image is difficult to reflect objectively. On this basis, this study combines wavelet transform and CNN method to propose an image quality evaluation method based on wavelet CNN. The low-frequency, horizontal, vertical, and diagonal sub-band images decomposed by wavelet transform are selected as the inputs of convolution neural network. The feature information in multiple directions is extracted by convolution neural network. Then, the information entropy of each sub-band image is calculated and used as the weight of each sub-band image quality. Finally, the quality evaluation values of four sub-band images are weighted and fused to obtain the visual quality values of the entire image. Experimental results show that the proposed method gains advantage from the global and local information of the image, thereby further improving its effectiveness and generalization.

Image quality evaluation of a small animal PET scanner

2020 ◽  
Vol 8 (1) ◽  
Author(s):  
Andréa Vidal Ferreira ◽  
Rodrigo Modesto Gadelha Gontijo ◽  
Guilherme Cavalcante de Albuquerque Souza ◽  
Bruno Melo Mendes ◽  
Juliana Batista da Silva ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Evaluation ◽  
Small Animal ◽  
Small Animal Pet ◽  
Image Quality Evaluation ◽  
Animal Pet

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